Semi-supervised dimensionality reduction via multimodal matrix factorization

Abstract

This paper presents a matrix factorization method for dimensionality reduction, semi-supervised two-way multimodal online matrix factorization (STWOMF). This method performs a semantic embedding by finding a linear mapping to a low dimensional semantic space modeled by the original high dimensional feature representation and the label space. An important characteristic of the proposed algorithm is that the new representation can be learned in a semi-supervised fashion. So, annotated instances are used to maximize the discrimination between classes, but also, non-annotated instances can be exploited to estimate the intrinsic manifold structure of the data. Another important advantage of this algorithm is its online formulation that allows to deal with large-scale collections by keeping low computational requirements. According with the experimental evaluation, the proposed STWOMF in comparison with several linear supervised, unsupervised and semisupervised dimensionality reduction methods, presents a competitive performance in classification while having a lower computational cost.